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High-spatial-resolution remote sensing image segmentation using adaptive watershed-driven joint MDEDNet
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DOI：10.1080/10106049.2022.2038694.png)
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When segmenting high-spatial-resolution remote sensing images using convolution neural networks, it is difficult to balance the number of segmented regions against the contour accuracy. This article proposes a segmentation method that combines the minimum area adaptive watershed transform based on morphological reconstruction with the modified deep edge detection network (MDEDNet). First, the minimum area adaptive watershed algorithm is adopted to reconstruct the gradient features of the image and perform watershed segmentation, giving the initial segmentation results. A ground object contour optimization method and MDEDNet are then used to further improve the accuracy of the segmentation, resulting in the final segmented image. Experiments on image sets of varying complexity indicate that the algorithm proposed in this article can achieve accurate image segmentation with good segmentation results and fast operation efficiency.
Keyword:
Deep convolution neural network model
morphological reconstruction
minimum area
adaptive watershed transform
MDEDNet
期刊
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3.5
论文数:
2.4K
被引数:
6.9K
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